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SUMMARY:L_p-Spherically Symmetric and L_p-nested Distributions for Patches
  of Natural Images - Fabian Sinz\, Max Planck Institute Tübingen
DTSTART:20100129T110000Z
DTEND:20100129T120000Z
UID:TALK22991@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:*Abstract:* In this talk I will present our recent work on mod
 elling patches of natural images with the class of Lp-sherically symmetric
  distributions and extension thereof. This class of distributions has many
  favorable properties like efficient maximum likelihood estimators and an 
 easy sampling scheme. It not only contains the factorial Laplacian or the 
 spherically symmetric model which are frequently used in image processing\
 , but also a whole variety of intermediate models. I will demonstrate how 
 this class gives rise to a simple non-linear mechanism severly reducing th
 e higher order dependencies between linear filter responses on natural ima
 ges. Finally\, I will present a generalization of that class by replacing 
 a single Lp-norm by an arbitrary cascade of Lp-norms. This class---called 
 Lp-nested symmetric distributions---offers more flexibility while preservi
 ng the nice properties of Lp-spherically symmetric distributions. Addition
 ally\, it contains the probability models corresponding to mixed-norm regu
 larizers which have recently gained increasing attention. 
LOCATION:Small public lecture room\, Microsoft Research Ltd\, 7 J J Thomso
 n Avenue (Off Madingley Road)\, Cambridge
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